The process industries, such as chemical, petroleum, power, food, textile, paper, and metallurgical, continuously or semi-continuously process gases, liquids, or solids. Electronic and mechanical equipment generally measures, indicates, and controls flow, pressure, temperature, level, and composition of these various gases, liquids, or solids. An industrial control system monitors an industrial process and makes changes or adjustments to maintain performance within certain acceptable conditions or limits. Measurement and control of process variables related to these gases, liquids, or solids range from indication and/or regulation of a single process variable to the optimization of the kinetics and throughput of hundreds of variables in an entire industrial plant.
A continuing trend of process manufacturing is to provide maximum profit per unit time of process operation. This emphasis demands larger combinations of assembled equipment and system configurations for industrial process control in order to monitor more data simultaneously; make more efficient control of interactive variables possible; present more information to an operator; insure a high level of availability of the process and continuity of process operation; allow for lower cost expansion, both vertically (i.e., compatibility with high-level computing equipment) and horizontally (i.e., system-size expansion); and facilitate entry of more external data.
Although different control strategies for various industrial process systems have been developed which emphasize different performance characteristics of the system, the functions normally required of the control system include process data acquisition, alarm for abnormal conditions, display and recording of process measurement, set point and output values, single-variable control using standard feedback algorithms, and multi-variable control including cascade, ratio, feedforward, and interacting system configurations. Process-control equipment has continually developed in response to this variety of functional requirements. Advances in electronic component technology and design techniques have facilitated the development of single processing subsystems in electronic devices and have also incorporated distributed information processing in measurement equipment. These advancements have, in turn, enhanced control system precision and flexibility.
An industrial process controller in an industrial process system conventionally establishes the acceptable conditions or limits of performance, often by setting its own reference inputs, and performs various functions on process variables to thereby generate control signals to operate actuators in the control system. These functions may include scaling, linearizing, shaping, algebraic computing, dynamic compensation, signal characterizing (i.e., adjusting lead and lag transfer functions or curve fitting for non-linear functions), or time-function generating for batch operations (i.e., ramp generating or signal programming). The plant or the controlled system is the part of the system that responds to the controller. The control functions of the controller may operate on the process variables in a variety of steady-state and dynamic control modes. The control functions may also hold process variables at predetermined set points or values by manipulating associated control elements in the system.
In an industrial process system in which a process variable is desirably maintained at a set point, a controller compares a measured process signal with the set point and calculates a control signal to minimize the difference. The control functions for such controllers can be implemented in many media, such as pneumatic, mechanical, hydraulic, and electronic analog and digital.
A proportional ("P") control function is one of the most widely known and simplest forms of continuous controls wherein the output control signal is proportional to the deviation between a measured process signal and a set point. Proportional control never brings a process back to the set point. It conventionally maintains the deviation between the set point and process variable. A gain in a process system occurs when the deviation or offset between the process variable and set point increases. A low gain is necessary to maintain process stability and allows for larger offsets from the set point to occur if the measured process signal changes quickly.
Another known control functions is the combination of proportional plus integral ("P+I") functions. This combination provides a wide dynamic proportional band to achieve process stability and high static gain to minimize high offsets at a rate tuned to the process variable dynamics. The P+I function, for example, may be used on flow control loops or at various levels in pressure loops of a control system.
Any control function which includes an integral ("I") function, however, is subject to windup or saturation when a deviation between a measured signal and a set point persists. Although this situation can be somewhat tolerated in a continuous process under control, it is often desired to be prevented on shut down and start up of batch processes. If saturation is not prevented, no control occurs on the start up until the measured signal reaches the set point value. In other words, the process variable may overshoot the set point by an appreciable amount. Thus, when the sequence of process operation calls for automatic start up, all control functions for the new action are within proportional limits. Overshoot is eliminated if the integral time exceeds the time constant of the measured process variable.
Some industrial processes also have several processing steps in series and can be more successfully controlled by the addition of a derivative ("D") or a phase lead function to the P+I function. Temperature control, for example, often requires a P+I+D or PID control function to compensate for the time lags of heaters, vessels, and sensors.
In other control processes, an offset between a measured signal and a set point must be determined before control actually occurs. If the major causes or factors of process variable change can be determined, it is possible to act directly on the measured signal to compensate for a change before its effect is imparted to the control signal and a deviation is caused between the set point and the process variable. This action is known as feedforward control. A feedforward control function is an approximate model of the process. Although it is theoretically possible to use feedforward control only, it is far more realistic to use a combination of feedforward and feedback control functions. The feedforward elements thus reduce the deviation seen by the feedback system, which then only has to correct for the imperfections in the feedforward process model. This technique is particularly applicable to processes having significant dead time, i.e., slow or no response to a measurement.
In a conventional process feedback system, a proportional-plus-integral-plus-derivative (PID) operation reacts to a deviation between a set point and a measured process variable. The PID control, however, has two major problems. First, PID control is purely reactive to process changes; it always trails in time an upset point, i.e., where the measured error starts. Second, oscillations or swings in the process are quite often induced by the PID because of the stored energy in the integral and/or derivative. Often, the system is susceptible to the release of the stored energy of the PID at the wrong time, such as when the process suddenly changes direction, which results in an oscillation.
There are several techniques available to assist in resolving these problems, such as gain that increases with error or integral that increases with error, or self tuning PID's. But the basic problems still exist, the PID is reactive and prone to induced oscillations or swings.
Characterization of the process feedback is common, often necessary, and is done in various ways using signal characterizing that is fixed; or a data collection system to make continuously updated graphical plots of signal characterizers. Characterization of the process demand is also a known technique that is available in systems such as damper linkage variations. Characterization of the process demand is available in recent control systems in parts of systems called variable gain adjustments or variable gain curves that may or may not be automatically adjusted through data collection.
Feedforward control is an improvement over PID control because it is proactive. Feedforward control is common and is done in different ways. The feedforward sometimes has a gain included to help balance the demand and process. The feedforward signal is often run through the PID as a cascade loop, and enters into the control loop as a set point adjustment. The feedforward signal may also be introduced into a control loop after the PID as a sum to the PID output.
The feedforward timing, however, is not always in phase with the process and therefore can cause stability and overshoot problems. To counter balance this tendency, the feedforward conventionally is de-emphasized or detuned. Reset windup or saturation occurs because the error signal to the PID stays offset during feedforward changes, and the reset integrates excessively. This results in swings at the end of the ramp or when the feedforward changes direction because the integral has to unwind.
The conventional feedforward configuration also induces an error into the system because of timing and/or gain mismatch to the process. The PID has to take out or compensate for the error induced by the improper feedforward. This causes process upsets, swings, and oscillations during and after feedforward changes which are highly undesirable in process control systems.